Databricks Data Engineer

CRG; United States; On-site; USD 90,000 - 94,000; Full time

Job summary

CRG is seeking a highly skilled Databricks Data Engineer to design, build, and optimize scalable data solutions within an Azure environment. You will implement end-to-end pipelines using Databricks, PySpark, and Kafka streaming, with a focus on CDC and real-time processing.

The role requires hands-on expertise to contribute immediately, collaborating with architects and product owners across Agile teams. You will drive data quality, performance, and scalability across diverse data sources.

Qualifications

• Hands-on Databricks experience with day-one contribution.
• End-to-end data pipelines with CDC capabilities.
• Strong PySpark and SQL proficiency; Python a plus.

Responsibilities

• Design, build, and optimize ETL/ELT pipelines using Databricks and PySpark.
• Develop scalable data solutions on Azure with Delta Lake and Kafka streaming.
• Implement CDC for incremental data processing and real-time needs.
• Understand existing codebases to deliver improvements quickly.
• Collaborate with architects, developers, and product owners in Agile/Scrum.
• Automate workflows using Databricks Workflows and CI/CD (GitHub).

Skills

Databricks

PySpark

SQL

Python

Azure

CDC

Kafka

Tools

Delta Lake

Unity Catalog

GitHub

Job description

$65-$68/hour W2 (No C2C or Sponsorship available)

We are seeking a highly skilled Databricks Data Engineer to support the Retail Spine initiative, working with a wide range of retail datasets to design, build, and optimize scalable data solutions. This role requires deep hands‑on expertise in Databricks and the ability to contribute immediately by developing new pipelines while enhancing existing data workflows. The ideal candidate brings extensive experience building end‑to‑end data engineering pipelines with CDC (Change Data Capture) capabilities and thrives in a fast‑paced Azure‑based environment leveraging Databricks, PySpark, and Kafka streaming.

Key Responsibilities

• Design, build, and optimize end‑to‑end ETL/ELT pipelines using Databricks and Apache Spark (PySpark).
• Develop and maintain scalable data solutions on Microsoft Azure, leveraging Databricks, Delta Lake, and Kafka streaming.
• Build robust pipelines with Change Data Capture (CDC) to support incremental data processing and real‑time/near real‑time data needs.
• Work across both new pipeline development and enhancement of existing pipelines, quickly understanding current codebases to deliver improvements.
• Integrate and process data from diverse sources including:
• Flat files, XML, JSON, SQL Server and JDBC systems, Kafka topics, APIs, MQ, Unity Catalog volumes and other enterprise platforms.
• Implement data quality frameworks, including writing technical data quality rules and quarantining error records.
• Apply canonical data modeling techniques, including SCD Type 1 and Type 2 implementations.
• Ensure high levels of data reliability, performance, scalability, and monitoring across all workflows.
• Automate workflows using Databricks Workflows and CI/CD pipelines (GitHub), focusing on reuse, efficiency, and standardization.
• Collaborate with developers, architects, and product owners, and engage with stakeholders, source teams, and downstream consumers as needed.
• Participate actively in Agile/Scrum ceremonies and deliver high‑quality solutions within defined timelines.

Required Qualifications

• Extensive hands‑on experience with Databricks, with ability to contribute from day one.
• Proven experience building end‑to‑end data engineering pipelines with CDC capabilities.
• Strong proficiency in PySpark and SQL (required); Python experience is a plus.
• Experience working in Microsoft Azure cloud environments.

Knowledge Of

• Distributed data processing frameworks
• Streaming technologies (Kafka)
• Data modeling (SCD Type 1 & Type 2)
• Experience integrating data from multiple structured and unstructured data sources.
• Familiarity with GitHub and CI/CD pipelines for version control and deployment.
• Strong understanding of data architecture, performance optimization, and pipeline scalability.